"""simple docstring"""



from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
SCREAMING_SNAKE_CASE__:List[Any]									=			200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller than N_POPULATION.
SCREAMING_SNAKE_CASE__:Optional[int]									=			50
# Probability that an element of a generation can mutate, changing one of its genes.
# This will guarantee that all genes will be used during evolution.
SCREAMING_SNAKE_CASE__:List[str]									=			0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1000))



def 	_lowerCamelCase(	a   ,    a				):
       __a       =    len([g for position, g in enumerate(a				) if g == main_target[position]]				)
       return (item, float(a				))



def 	_lowerCamelCase(	a   ,    a				):
       __a       =    random.randint(0   ,    len(a				) - 1				)
       __a       =    parent_a[:random_slice] + parent_a[random_slice:]
       __a       =    parent_a[:random_slice] + parent_a[random_slice:]
       return (child_a, child_a)



def 	_lowerCamelCase(	a   ,    a				):
       __a       =    list(a				)
       if random.uniform(0   ,    1				) < MUTATION_PROBABILITY:
              __a       =    random.choice(a				)
       return "".join(a				)



def 	_lowerCamelCase(	a   ,    a   ,    a   ,    ):
       __a       =    []
       # Generate more children proportionally to the fitness score.
       __a       =    int(parent_a[1] * 1_0_0				) + 1
       __a       =    1_0 if child_n >= 1_0 else child_n
       for _ in range(a				):
              __a       =    population_score[random.randint(0   ,    a				)][0]

              __a ,   __a       =    crossover(parent_a[0]   ,    a				)
              # Append new string to the population list.
              pop.append(mutate(a   ,    a				)				)
              pop.append(mutate(a   ,    a				)				)
       return pop



def 	_lowerCamelCase(	a   ,    a   ,    a = True				):

       # Verify if N_POPULATION is bigger than N_SELECTED
       if N_POPULATION < N_SELECTED:
              __a       =    F"{N_POPULATION} must be bigger than {N_SELECTED}"
              raise ValueError(a				)
       # Verify that the target contains no genes besides the ones inside genes variable.
       __a       =    sorted({c for c in target if c not in genes}				)
       if not_in_genes_list:
              __a       =    F"{not_in_genes_list} is not in genes list, evolution cannot converge"
              raise ValueError(a				)

       # Generate random starting population.
       __a       =    []
       for _ in range(a				):
              population.append("".join([random.choice(a				) for i in range(len(a				)				)]				)				)

       # Just some logs to know what the algorithms is doing.
       __a ,   __a       =    0, 0

       # This loop will end when we find a perfect match for our target.
       while True:
              generation += 1
              total_population += len(a				)

              # Random population created. Now it's time to evaluate.

              # Adding a bit of concurrency can make everything faster,
              #
              # import concurrent.futures
              # population_score: list[tuple[str, float]] = []
              # with concurrent.futures.ThreadPoolExecutor(
              #                                   max_workers=NUM_WORKERS) as executor:
              #     futures = {executor.submit(evaluate, item) for item in population}
              #     concurrent.futures.wait(futures)
              #     population_score = [item.result() for item in futures]
              #
              # but with a simple algorithm like this, it will probably be slower.
              # We just need to call evaluate for every item inside the population.
              __a       =    [evaluate(a   ,    a				) for item in population]

              # Check if there is a matching evolution.
              __a       =    sorted(a   ,    key=lambda a				: x[1]   ,    reverse=a				)
              if population_score[0][0] == target:
                     return (generation, total_population, population_score[0][0])

              # Print the best result every 10 generation.
              # Just to know that the algorithm is working.
              if debug and generation % 1_0 == 0:
                     print(
                         F"\nGeneration: {generation}"
                         F"\nTotal Population:{total_population}"
                         F"\nBest score: {population_score[0][1]}"
                         F"\nBest string: {population_score[0][0]}"				)

              # Flush the old population, keeping some of the best evolutions.
              # Keeping this avoid regression of evolution.
              __a       =    population[: int(N_POPULATION / 3				)]
              population.clear()
              population.extend(a				)
              # Normalize population score to be between 0 and 1.
              __a       =    [
                  (item, score / len(a				)) for item, score in population_score
              ]

              # This is selection
              for i in range(a				):
                     population.extend(select(population_score[int(a				)]   ,    a   ,    a				)				)
                     # Check if the population has already reached the maximum value and if so,
                     # break the cycle.  If this check is disabled, the algorithm will take
                     # forever to compute large strings, but will also calculate small strings in
                     # a far fewer generations.
                     if len(a				) > N_POPULATION:
                            break


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE__:List[Any]									=			(
       """This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!"""
   )
   SCREAMING_SNAKE_CASE__:Optional[int]									=			list(
       """ ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm"""
       """nopqrstuvwxyz.,;!?+-*#@^'èéòà€ù=)(&%$£/\\"""
   )
   SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__:Optional[Any]									=			basic(target_str, genes_list)
   print(
       F'''\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}'''
   )

"""simple docstring"""



import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__:Tuple									=			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__:Optional[int]									=			{
    """asapp/sew-d-tiny-100k""": """https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json""",
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}






class  snake_case__      (       snake_case_       ):
 _snake_case       :					Union[str, Any]							  =						"""sew-d"""


 def __init__( self    ,		lowerCamelCase=32    ,		lowerCamelCase=768    ,		lowerCamelCase=12    ,		lowerCamelCase=12    ,		lowerCamelCase=3072    ,		lowerCamelCase=2    ,		lowerCamelCase=512    ,		lowerCamelCase=256    ,		lowerCamelCase=True    ,		lowerCamelCase=True    ,		lowerCamelCase=("p2c", "c2p")    ,		lowerCamelCase="layer_norm"    ,		lowerCamelCase="gelu_python"    ,		lowerCamelCase=0.1    ,		lowerCamelCase=0.1    ,		lowerCamelCase=0.1    ,		lowerCamelCase=0.0    ,		lowerCamelCase=0.1    ,		lowerCamelCase=0.02    ,		lowerCamelCase=1E-7    ,		lowerCamelCase=1E-5    ,		lowerCamelCase="group"    ,		lowerCamelCase="gelu"    ,		lowerCamelCase=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)    ,		lowerCamelCase=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)    ,		lowerCamelCase=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)    ,		lowerCamelCase=False    ,		lowerCamelCase=128    ,		lowerCamelCase=16    ,		lowerCamelCase=True    ,		lowerCamelCase=0.05    ,		lowerCamelCase=10    ,		lowerCamelCase=2    ,		lowerCamelCase=0.0    ,		lowerCamelCase=10    ,		lowerCamelCase=0    ,		lowerCamelCase="mean"    ,		lowerCamelCase=False    ,		lowerCamelCase=False    ,		lowerCamelCase=256    ,		lowerCamelCase=0    ,		lowerCamelCase=1    ,		lowerCamelCase=2    ,		**lowerCamelCase    ,		):
        super().__init__(**lowerCamelCase    ,		pad_token_id=lowerCamelCase    ,		bos_token_id=lowerCamelCase    ,		eos_token_id=lowerCamelCase						)
        __a       =    hidden_size
        __a       =    feat_extract_norm
        __a       =    feat_extract_activation
        __a       =    list(lowerCamelCase						)
        __a       =    list(lowerCamelCase						)
        __a       =    list(lowerCamelCase						)
        __a       =    conv_bias
        __a       =    num_conv_pos_embeddings
        __a       =    num_conv_pos_embedding_groups
        __a       =    len(self.conv_dim						)
        __a       =    num_hidden_layers
        __a       =    intermediate_size
        __a       =    squeeze_factor
        __a       =    max_position_embeddings
        __a       =    position_buckets
        __a       =    share_att_key
        __a       =    relative_attention
        __a       =    norm_rel_ebd
        __a       =    list(lowerCamelCase						)
        __a       =    hidden_act
        __a       =    num_attention_heads
        __a       =    hidden_dropout
        __a       =    attention_dropout
        __a       =    activation_dropout
        __a       =    feat_proj_dropout
        __a       =    final_dropout
        __a       =    layer_norm_eps
        __a       =    feature_layer_norm_eps
        __a       =    initializer_range
        __a       =    vocab_size

        if (
            (len(self.conv_stride						) != self.num_feat_extract_layers)
            or (len(self.conv_kernel						) != self.num_feat_extract_layers)
            or (len(self.conv_dim						) != self.num_feat_extract_layers)
        ):
               raise ValueError(
                   "Configuration for convolutional layers is incorrect."
                   "It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"
                   F"but is `len(config.conv_dim) = {len(self.conv_dim						)}`, `len(config.conv_stride)"
                   F"= {len(self.conv_stride						)}`, `len(config.conv_kernel) = {len(self.conv_kernel						)}`."						)

        # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
        __a       =    apply_spec_augment
        __a       =    mask_time_prob
        __a       =    mask_time_length
        __a       =    mask_time_min_masks
        __a       =    mask_feature_prob
        __a       =    mask_feature_length
        __a       =    mask_feature_min_masks

        # ctc loss
        __a       =    ctc_loss_reduction
        __a       =    ctc_zero_infinity

        # sequence classification
        __a       =    use_weighted_layer_sum
        __a       =    classifier_proj_size






 @property
 def 						a__   ( self						):
        return functools.reduce(operator.mul    ,		self.conv_stride    ,		1						)
